PhD Chapter 3

Results 2/3


This series of files compile all analyses done during Chapter 3:

All analyses have been done with R 4.1.0.

Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it

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1. Spatial variation of exposure indices

Here, we compute semivariograms for each exposure index (on the whole raster, not only extracted values at the stations).

Aquaculture
## Model selected: Lin
## nugget = 0; sill = 0.01241; range = 2.73059; kappa = 0.5

Dredging
## Model selected: Sph
## nugget = 0; sill = 0.0203; range = 4.79517; kappa = 0.5

Runoff
## Model selected: Lin
## nugget = 0; sill = 0.08706; range = 8.41091; kappa = 0.5

Sewers
## Model selected: Sph
## nugget = 0; sill = 0.23622; range = 54.32337; kappa = 0.5

Structures
## Model selected: Sph
## nugget = 0; sill = 0.54944; range = 77.74397; kappa = 0.5

Shipping
## Model selected: Lin
## nugget = 0; sill = 0.07514; range = 4.06682; kappa = 0.5

Fisheries
## Model selected: Lin
## nugget = 0; sill = 0.02622; range = 3.51952; kappa = 0.5

2. Relationships with abiotic parameters

2.1. Covariation

Several types of models were considered to explore relationships: linear, quadratic, exponential and logarithmic. The model with the highest \(R^{2}\) is presented on each plot.

⚠️ Only linear models are implemented now, as there are some bugs with the automatized calculation of the others.

Aquaculture

Dredging

Runoff

Sewers

Structures

Shipping

Fisheries

Cumulative exposure

2.2. Correlation

Correlations have been calculated with Spearman’s rank coefficient.

Correlation coefficients between exposure indices and ecosystem variables
  om gravel sand silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc S N B H J
aquaculture -0.088 0.065 0.023 -0.025 0.051 -0.282 -0.24 -0.355 -0.476 -0.483 -0.479 -0.286 -0.267 -0.413 0.152 -0.02 -0.055 0.209 0.157
dredging 0.238 -0.02 -0.012 0.003 0.044 0.062 0.004 0.257 0.373 0.566 0.369 -0.025 0.126 0.28 -0.164 -0.167 0.107 -0.05 0.052
runoff -0.016 -0.075 0.297 -0.194 0.018 -0.149 -0.093 0.048 0.299 0.278 0.154 -0.126 -0.027 0.147 -0.162 -0.059 -0.068 -0.065 0.03
sewers 0.424 -0.14 -0.387 0.344 0.164 0.639 0.588 0.691 0.751 0.669 0.755 0.642 0.706 0.743 -0.248 -0.091 0.053 -0.23 -0.108
structures 0.172 -0.071 0.045 -0.006 0.076 0.052 0.044 0.277 0.465 0.514 0.404 0.057 0.165 0.326 -0.219 -0.141 0.003 -0.109 0.012
shipping 0.371 -0.223 -0.228 0.238 -0.08 0.415 0.27 0.464 0.573 0.57 0.55 0.406 0.415 0.542 -0.059 -0.071 0.054 -0.035 -0.036
fisheries -0.492 0.202 0.376 -0.378 -0.138 -0.567 -0.541 -0.552 -0.606 -0.576 -0.585 -0.54 -0.563 -0.613 0.309 0.173 -0.066 0.224 -0.015
cumulative_exposure 0.277 -0.108 -0.058 0.086 0.087 0.19 0.144 0.357 0.556 0.58 0.471 0.196 0.298 0.445 -0.164 -0.092 0.03 -0.107 -0.048
p-values of correlation test between exposure indices and ecosystem variables
  om gravel sand silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc S N B H J
aquaculture 0.3664 0.5051 0.8152 0.7992 0.6021 0.003082 0.01234 0.0001626 1.949e-07 1.228e-07 1.579e-07 0.002721 0.005256 8.895e-06 0.1157 0.8385 0.5693 0.02961 0.1049
dredging 0.01324 0.8387 0.9003 0.9732 0.6495 0.5259 0.9659 0.00727 6.998e-05 1.771e-10 8.557e-05 0.799 0.1938 0.003346 0.09076 0.08431 0.269 0.6071 0.594
runoff 0.8701 0.4395 0.001802 0.04443 0.8515 0.1241 0.3393 0.623 0.001656 0.003518 0.1125 0.1942 0.7832 0.1285 0.09401 0.5414 0.4829 0.5056 0.7592
sewers 4.879e-06 0.1485 3.54e-05 0.0002644 0.09067 9.873e-14 2.137e-11 1.233e-16 7.406e-21 2.565e-15 3.667e-21 6.987e-14 1.478e-17 3.514e-20 0.00972 0.3471 0.5842 0.01674 0.2654
structures 0.07492 0.4645 0.6446 0.9475 0.4369 0.5895 0.6483 0.003746 3.933e-07 1.315e-08 1.425e-05 0.5586 0.08784 0.0005685 0.02293 0.1453 0.9766 0.2634 0.8993
shipping 7.89e-05 0.02058 0.01742 0.01331 0.4089 7.945e-06 0.004671 4.107e-07 9.46e-11 1.253e-10 7.297e-10 1.302e-05 7.806e-06 1.344e-09 0.5475 0.4653 0.5813 0.7225 0.7092
fisheries 6.275e-08 0.03585 6.17e-05 5.585e-05 0.1551 1.607e-10 1.476e-09 6.146e-10 3.727e-12 6.679e-11 2.989e-11 1.623e-09 2.366e-10 1.852e-12 0.001128 0.07296 0.496 0.01998 0.8735
cumulative_exposure 0.003733 0.2652 0.5492 0.3756 0.3707 0.04849 0.1364 0.0001492 4.022e-10 4.899e-11 2.628e-07 0.04167 0.001703 1.356e-06 0.08962 0.3421 0.7614 0.2702 0.6214

3. Relationships with benthic communities

3.1. Taxa identity

The most abundant taxa in our study area are:

  • Density: B.neotena (1969), E. integra (1158), P.grandimana (1092), Nematoda (1044) and M. calcarea (575)
  • Biomass: E. parma (biomass of 531.5), Strongylocentrotus sp. (65.3), N. incisa (58.5), M. calcarea (45.4) and S. groenlandicus (34.3)

The following graphs present the distribution of sampled phyla along index of cumulative exposure, according to density (left side) or biomass (right side).

Exposure categories are based on the exposure index: the higher the index, the lower the status. Five exposure categories (‘bad’, ‘poor’, ‘moderate’, good’, ‘high’) have been set relative to the exposure index (5.6 ≤ \(E\), 4.2 ≤ \(E\) < 5.6, 2.8 ≤ \(E\) < 4.2, 1.4 ≤ \(E\) < 2.8, \(E\) < 1.4, respectively). Maximum detected cumulative exposure is 3.698.

By exposure gradient

By exposure categories

Phylum mean density by group
Phylum low bad moderate high good
Annelida NA NA 24.3 34.1 27.9
Arthropoda NA NA 24.4 62.7 29.9
Cnidaria NA NA 0 0 0.025
Echinodermata NA NA 0.389 1.92 5.55
Mollusca NA NA 11.5 13.5 14.8
Nematoda NA NA 0 4.9 20
Nemertea NA NA 0 0.08 0.3
Sipuncula NA NA 0.111 0.4 0.175
Phylum mean biomass by group
Phylum low bad moderate high good
Annelida NA NA 4.03 0.855 0.49
Arthropoda NA NA 0.0778 0.114 0.158
Cnidaria NA NA 0 0 0.0841
Echinodermata NA NA 4.5 1.25 11.4
Mollusca NA NA 1.75 1.56 1.11
Nematoda NA NA 0 0.000286 0.000867
Nemertea NA NA 0 0.0342 5.5e-05
Sipuncula NA NA 0.00468 0.0111 0.0114

3.2. Community characteristics

The following graphs present the distribution of community characteristics along index of cumulative exposure.

Exposure categories are based on the exposure index: the higher the index, the lower the status. Maximum cumulative exposure is 3.698. Five exposure categories from ‘bad’ to ‘high’ have been set with 20 %, 40 %, 60 % or 80 % of the maximum exposure.

By exposure gradient

By exposure categories

4. Regressions

For the following analyses, independant variables are abiotic parameters and exposure indices, dependant variables are community characteristics. Variables have been standardized by mean and standard-deviation.

4.1. Data manipulation

All stations and predictors were selected for the regressions, as we are interested in each of them (following graphs are for information only).

Correlation coefficients between exposure indices
  aquaculture dredging runoff sewers structures shipping fisheries
aquaculture 1 -0.291 -0.536 -0.572 -0.498 -0.485 0.456
dredging -0.291 1 0.595 0.409 0.776 0.463 -0.28
runoff -0.536 0.595 1 0.37 0.866 0.288 -0.302
sewers -0.572 0.409 0.37 1 0.601 0.735 -0.686
structures -0.498 0.776 0.866 0.601 1 0.469 -0.377
shipping -0.485 0.463 0.288 0.735 0.469 1 -0.53
fisheries 0.456 -0.28 -0.302 -0.686 -0.377 -0.53 1

4.2. Univariate regressions

We used linear models for the regressions on community characteristics. Variables have been standardized by mean and standard-deviation (coefficients need to be back-transformed to be used in predictive models). Variable selection was not needed here, as we are interested in all exposure indices.

Results of regressions (coefficients with a significant p-value for marginal tests) are shown below. Using both abiotic parameters and exposure indices as predictors do not increase significantly predictive power compared to the other models. Details of the regressions, with diagnostics and cross-validation, are summarized below.

All variables
Predictor S N B H J
Depth + +
OM
Gravel
Silt
Clay
Arsenic
Cadmium -
Chromium
Copper - -
Iron -
Manganese
Mercury
Lead
Zinc + +
Aquaculture
Dredging
Runoff + +
Sewers
Structures +
Shipping +
Fisheries +
Adjusted \(R^{2}\) 0.23 0.02 0.2 0.33 0.12
Richness
## Adjusted R2 is: 0.23
Fitting linear model: S ~ depth + om + gravel + silt + clay + arsenic + cadmium + chromium + copper + iron + manganese + mercury + lead + zinc + aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -6.409e-16 0.08465 -7.57e-15 1
depth 0.1302 0.1277 1.02 0.3108
om 0.3336 0.1842 1.811 0.07358
gravel -0.04503 0.1092 -0.4124 0.6811
silt -0.006452 0.1714 -0.03764 0.9701
clay 0.007711 0.1058 0.07289 0.9421
arsenic 0.07328 0.2072 0.3537 0.7244
cadmium -0.1993 0.3127 -0.6374 0.5256
chromium -0.2509 0.5132 -0.4889 0.6261
copper -0.4804 0.8275 -0.5806 0.5631
iron -0.3204 0.1425 -2.248 0.02712 *
manganese 0.3417 0.2987 1.144 0.2557
mercury -0.03731 0.1854 -0.2013 0.841
lead -0.03209 0.5565 -0.05767 0.9541
zinc 0.3722 0.7441 0.5001 0.6183
aquaculture 0.1093 0.1356 0.806 0.4225
dredging -0.2548 0.1913 -1.332 0.1864
runoff 0.2944 0.2826 1.042 0.3005
sewers -0.07597 0.2248 -0.338 0.7362
structures -0.05046 0.2796 -0.1805 0.8572
shipping 0.3021 0.147 2.056 0.04283 *
fisheries 0.2629 0.1099 2.392 0.01892 *
## RMSE from cross-validation: 1.026268
Variance Inflation Factors
  depth om gravel silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc aquaculture dredging runoff sewers structures shipping fisheries
VIF 1.5 2.17 1.28 2.02 1.24 2.44 3.68 6.03 9.73 1.68 3.51 2.18 6.54 8.75 1.59 2.25 3.32 2.64 3.29 1.73 1.29

Density
## Adjusted R2 is: 0.02
Fitting linear model: N ~ depth + om + gravel + silt + clay + arsenic + cadmium + chromium + copper + iron + manganese + mercury + lead + zinc + aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.738e-16 0.0954 4.967e-15 1
depth -0.23 0.1439 -1.598 0.1137
om 0.1904 0.2075 0.9173 0.3615
gravel 0.04305 0.123 0.3499 0.7273
silt -0.1865 0.1932 -0.9654 0.3371
clay -0.1854 0.1192 -1.555 0.1236
arsenic 0.07132 0.2334 0.3055 0.7607
cadmium 0.4086 0.3524 1.16 0.2494
chromium -0.7084 0.5784 -1.225 0.224
copper 0.8192 0.9325 0.8785 0.3821
iron -0.1265 0.1606 -0.7879 0.4329
manganese 0.1497 0.3365 0.4448 0.6576
mercury -0.1331 0.2089 -0.6373 0.5256
lead 0.06011 0.6271 0.09586 0.9239
zinc -0.806 0.8386 -0.9611 0.3392
aquaculture -0.002814 0.1528 -0.01842 0.9853
dredging -0.1067 0.2156 -0.4947 0.6221
runoff -0.01846 0.3185 -0.05797 0.9539
sewers 0.2833 0.2533 1.119 0.2664
structures -0.1471 0.3151 -0.4669 0.6418
shipping -0.06749 0.1656 -0.4075 0.6846
fisheries 0.07969 0.1238 0.6437 0.5215
## RMSE from cross-validation: 1.118892
Variance Inflation Factors
  depth om gravel silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc aquaculture dredging runoff sewers structures shipping fisheries
VIF 1.5 2.17 1.28 2.02 1.24 2.44 3.68 6.03 9.73 1.68 3.51 2.18 6.54 8.75 1.59 2.25 3.32 2.64 3.29 1.73 1.29

Biomass
## Adjusted R2 is: 0.2
Fitting linear model: B ~ depth + om + gravel + silt + clay + arsenic + cadmium + chromium + copper + iron + manganese + mercury + lead + zinc + aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.36e-15 0.0863 -1.576e-14 1
depth -0.2313 0.1302 -1.776 0.07925
om 0.09402 0.1877 0.5008 0.6178
gravel 0.005369 0.1113 0.04823 0.9616
silt -0.1639 0.1748 -0.9377 0.351
clay -0.05424 0.1078 -0.5029 0.6163
arsenic 0.3814 0.2112 1.806 0.07439
cadmium -0.8774 0.3187 -2.753 0.007207 * *
chromium 0.4618 0.5232 0.8827 0.3799
copper -2.233 0.8435 -2.647 0.009659 * *
iron -0.1233 0.1453 -0.849 0.3983
manganese 0.01028 0.3044 0.03378 0.9731
mercury 0.3607 0.189 1.909 0.05961
lead -0.4934 0.5673 -0.8698 0.3869
zinc 2.013 0.7586 2.654 0.009466 * *
aquaculture -0.1576 0.1382 -1.141 0.2572
dredging -0.1426 0.1951 -0.7311 0.4667
runoff -0.4212 0.2881 -1.462 0.1473
sewers -0.01312 0.2291 -0.05725 0.9545
structures 0.6896 0.285 2.419 0.01765 *
shipping -0.04084 0.1498 -0.2726 0.7858
fisheries -0.07428 0.112 -0.6632 0.509
## RMSE from cross-validation: 1.172694
Variance Inflation Factors
  depth om gravel silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc aquaculture dredging runoff sewers structures shipping fisheries
VIF 1.5 2.17 1.28 2.02 1.24 2.44 3.68 6.03 9.73 1.68 3.51 2.18 6.54 8.75 1.59 2.25 3.32 2.64 3.29 1.73 1.29

Diversity
## Adjusted R2 is: 0.33
Fitting linear model: H ~ depth + om + gravel + silt + clay + arsenic + cadmium + chromium + copper + iron + manganese + mercury + lead + zinc + aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.652e-16 0.07896 -4.625e-15 1
depth 0.4534 0.1191 3.806 0.000264 * * *
om 0.295 0.1718 1.717 0.08952
gravel -0.07467 0.1019 -0.7331 0.4655
silt -0.01644 0.1599 -0.1028 0.9184
clay 0.14 0.09868 1.419 0.1596
arsenic 0.04943 0.1932 0.2558 0.7987
cadmium -0.5268 0.2917 -1.806 0.07438
chromium -0.1193 0.4787 -0.2492 0.8038
copper -1.503 0.7719 -1.947 0.05479
iron -0.2248 0.1329 -1.691 0.0945
manganese 0.4479 0.2786 1.608 0.1116
mercury 0.08304 0.1729 0.4802 0.6323
lead 0.1537 0.5191 0.2961 0.7678
zinc 1.343 0.6941 1.935 0.05628
aquaculture 0.1823 0.1265 1.442 0.1531
dredging -0.1584 0.1785 -0.8877 0.3772
runoff 0.6293 0.2636 2.387 0.01918 *
sewers -0.01829 0.2096 -0.08722 0.9307
structures -0.181 0.2608 -0.6941 0.4895
shipping 0.1713 0.1371 1.25 0.2147
fisheries 0.1266 0.1025 1.235 0.2202
## RMSE from cross-validation: 1.057078
Variance Inflation Factors
  depth om gravel silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc aquaculture dredging runoff sewers structures shipping fisheries
VIF 1.5 2.17 1.28 2.02 1.24 2.44 3.68 6.03 9.73 1.68 3.51 2.18 6.54 8.75 1.59 2.25 3.32 2.64 3.29 1.73 1.29

Evenness
## Adjusted R2 is: 0.12
Fitting linear model: J ~ depth + om + gravel + silt + clay + arsenic + cadmium + chromium + copper + iron + manganese + mercury + lead + zinc + aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.401e-16 0.09034 -5.978e-15 1
depth 0.469 0.1363 3.441 0.0008977 * * *
om -0.02793 0.1965 -0.1421 0.8873
gravel -0.0004933 0.1165 -0.004233 0.9966
silt 0.1079 0.183 0.5899 0.5568
clay 0.2154 0.1129 1.908 0.05977
arsenic 0.02584 0.2211 0.1169 0.9072
cadmium -0.6172 0.3337 -1.85 0.06779
chromium 0.1247 0.5477 0.2276 0.8205
copper -1.781 0.8831 -2.017 0.0468 *
iron 0.06011 0.1521 0.3952 0.6937
manganese 0.2591 0.3187 0.813 0.4185
mercury 0.1163 0.1978 0.588 0.5581
lead 0.3847 0.5939 0.6478 0.5189
zinc 1.586 0.7942 1.997 0.04898 *
aquaculture 0.1135 0.1447 0.7844 0.435
dredging -0.05131 0.2042 -0.2513 0.8022
runoff 0.6344 0.3016 2.103 0.03836 *
sewers -0.07083 0.2399 -0.2953 0.7685
structures -0.1869 0.2984 -0.6263 0.5328
shipping -0.0203 0.1568 -0.1294 0.8973
fisheries -0.04536 0.1173 -0.3869 0.6998
## RMSE from cross-validation: 1.13629
Variance Inflation Factors
  depth om gravel silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc aquaculture dredging runoff sewers structures shipping fisheries
VIF 1.5 2.17 1.28 2.02 1.24 2.44 3.68 6.03 9.73 1.68 3.51 2.18 6.54 8.75 1.59 2.25 3.32 2.64 3.29 1.73 1.29

Abiotic parameters
Predictor S N B H J
Depth + - - + +
OM +
Gravel
Silt
Clay
Arsenic +
Cadmium -
Chromium
Copper -
Iron -
Manganese
Mercury +
Lead
Zinc +
Adjusted \(R^{2}\) 0.18 0.06 0.18 0.32 0.12
Richness
## Adjusted R2 is: 0.18
Fitting linear model: S ~ depth + om + gravel + silt + clay + arsenic + cadmium + chromium + copper + iron + manganese + mercury + lead + zinc
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -6.177e-16 0.08689 -7.109e-15 1
depth 0.2315 0.1095 2.115 0.03713 *
om 0.2195 0.174 1.261 0.2105
gravel -0.04709 0.1087 -0.4334 0.6657
silt -0.03854 0.1634 -0.2358 0.8141
clay -0.01555 0.1032 -0.1507 0.8806
arsenic -0.05694 0.1667 -0.3416 0.7334
cadmium -0.1312 0.2652 -0.4948 0.6219
chromium -0.1282 0.4322 -0.2967 0.7674
copper -0.05041 0.5083 -0.09917 0.9212
iron -0.3674 0.1236 -2.973 0.003754 * *
manganese 0.1596 0.2594 0.6155 0.5397
mercury 0.06406 0.1519 0.4218 0.6742
lead -0.2005 0.4493 -0.4463 0.6564
zinc 0.1353 0.6344 0.2133 0.8316
## RMSE from cross-validation: 1.006118
Variance Inflation Factors
  depth om gravel silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc
VIF 1.25 1.99 1.24 1.87 1.18 1.91 3.04 4.95 5.82 1.42 2.97 1.74 5.15 7.27

Density
## Adjusted R2 is: 0.06
Fitting linear model: N ~ depth + om + gravel + silt + clay + arsenic + cadmium + chromium + copper + iron + manganese + mercury + lead + zinc
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.524e-16 0.09348 5.909e-15 1
depth -0.252 0.1178 -2.139 0.03505 *
om 0.1448 0.1873 0.773 0.4415
gravel 0.05044 0.1169 0.4314 0.6671
silt -0.1457 0.1758 -0.8289 0.4093
clay -0.1485 0.1111 -1.338 0.1843
arsenic 0.01773 0.1793 0.09886 0.9215
cadmium 0.4549 0.2853 1.594 0.1142
chromium -0.5273 0.4651 -1.134 0.2598
copper 0.7258 0.5469 1.327 0.1877
iron -0.1645 0.133 -1.237 0.2191
manganese 0.05942 0.2791 0.2129 0.8319
mercury -0.1153 0.1634 -0.7058 0.4821
lead 0.3046 0.4834 0.6302 0.5301
zinc -0.9698 0.6826 -1.421 0.1587
## RMSE from cross-validation: 1.033773
Variance Inflation Factors
  depth om gravel silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc
VIF 1.25 1.99 1.24 1.87 1.18 1.91 3.04 4.95 5.82 1.42 2.97 1.74 5.15 7.27

Biomass
## Adjusted R2 is: 0.18
Fitting linear model: B ~ depth + om + gravel + silt + clay + arsenic + cadmium + chromium + copper + iron + manganese + mercury + lead + zinc
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.001e-15 0.08716 -1.149e-14 1
depth -0.2195 0.1098 -1.999 0.04855 *
om 0.06075 0.1746 0.348 0.7286
gravel 0.01997 0.109 0.1832 0.855
silt -0.1704 0.1639 -1.039 0.3013
clay -0.04205 0.1035 -0.4061 0.6856
arsenic 0.3541 0.1672 2.118 0.03685 *
cadmium -0.6433 0.266 -2.419 0.01753 *
chromium 0.4378 0.4336 1.01 0.3153
copper -1.556 0.5099 -3.051 0.00297 * *
iron -0.01015 0.124 -0.08185 0.9349
manganese 0.07822 0.2602 0.3006 0.7644
mercury 0.3488 0.1524 2.289 0.02433 *
lead -0.8704 0.4507 -1.931 0.05648
zinc 1.625 0.6364 2.553 0.01229 *
## RMSE from cross-validation: 1.179323
Variance Inflation Factors
  depth om gravel silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc
VIF 1.25 1.99 1.24 1.87 1.18 1.91 3.04 4.95 5.82 1.42 2.97 1.74 5.15 7.27

Diversity
## Adjusted R2 is: 0.32
Fitting linear model: H ~ depth + om + gravel + silt + clay + arsenic + cadmium + chromium + copper + iron + manganese + mercury + lead + zinc
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.393e-16 0.07917 -1.76e-15 1
depth 0.4211 0.09976 4.222 5.643e-05 * * *
om 0.3487 0.1586 2.199 0.03038 *
gravel -0.1207 0.099 -1.219 0.2258
silt -0.1186 0.1489 -0.7969 0.4276
clay 0.08567 0.09404 0.911 0.3647
arsenic -0.08548 0.1519 -0.5628 0.5749
cadmium -0.2729 0.2416 -1.13 0.2615
chromium -0.4294 0.3938 -1.09 0.2784
copper -0.3445 0.4632 -0.7437 0.4589
iron -0.2159 0.1126 -1.918 0.05819
manganese 0.2222 0.2363 0.94 0.3497
mercury 0.1032 0.1384 0.7456 0.4578
lead -0.1383 0.4093 -0.3379 0.7362
zinc 0.749 0.5781 1.296 0.1983
## RMSE from cross-validation: 0.9988091
Variance Inflation Factors
  depth om gravel silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc
VIF 1.25 1.99 1.24 1.87 1.18 1.91 3.04 4.95 5.82 1.42 2.97 1.74 5.15 7.27

Evenness
## Adjusted R2 is: 0.12
Fitting linear model: J ~ depth + om + gravel + silt + clay + arsenic + cadmium + chromium + copper + iron + manganese + mercury + lead + zinc
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.246e-16 0.09028 -3.596e-15 1
depth 0.3458 0.1138 3.04 0.003077 * *
om 0.1431 0.1809 0.791 0.431
gravel -0.06604 0.1129 -0.5849 0.56
silt -0.02428 0.1698 -0.143 0.8866
clay 0.1515 0.1072 1.413 0.161
arsenic -0.08341 0.1732 -0.4816 0.6312
cadmium -0.3626 0.2755 -1.316 0.1914
chromium -0.3113 0.4491 -0.693 0.49
copper -0.6336 0.5282 -1.2 0.2333
iron 0.08102 0.1284 0.631 0.5296
manganese 0.07169 0.2695 0.266 0.7908
mercury 0.03764 0.1578 0.2385 0.8121
lead 0.1069 0.4668 0.2289 0.8194
zinc 1.026 0.6592 1.556 0.1232
## RMSE from cross-validation: 1.061198
Variance Inflation Factors
  depth om gravel silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc
VIF 1.25 1.99 1.24 1.87 1.18 1.91 3.04 4.95 5.82 1.42 2.97 1.74 5.15 7.27

Exposure indices
Predictor S N B H J
Depth + +
Aquaculture
Dredging
Runoff - +
Sewers - -
Structures +
Shipping +
Fisheries +
Adjusted \(R^{2}\) 0.16 0.02 0.04 0.29 0.14
Richness
## Adjusted R2 is: 0.16
Fitting linear model: S ~ aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.208e-16 0.0882 -2.504e-15 1
aquaculture 0.1387 0.1246 1.114 0.2681
dredging -0.1659 0.1192 -1.392 0.167
runoff 0.1802 0.2009 0.8969 0.3719
sewers -0.3168 0.1586 -1.998 0.04846 *
structures -0.05714 0.2315 -0.2468 0.8056
shipping 0.3368 0.1164 2.894 0.004668 * *
fisheries 0.2269 0.106 2.141 0.03474 *
## RMSE from cross-validation: 0.945198
Variance Inflation Factors
  aquaculture dredging runoff sewers structures shipping fisheries
VIF 1.41 1.34 2.27 1.79 2.61 1.31 1.2

Density
## Adjusted R2 is: 0.02
Fitting linear model: N ~ depth + aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.925e-16 0.09539 2.018e-15 1
depth -0.185 0.1122 -1.649 0.1024
aquaculture 0.05215 0.1347 0.3871 0.6995
dredging -0.1216 0.129 -0.9424 0.3483
runoff 0.1879 0.2201 0.854 0.3952
sewers 0.1503 0.1855 0.8101 0.4198
structures -0.1768 0.2539 -0.6963 0.4879
shipping -0.09248 0.133 -0.6955 0.4884
fisheries 0.122 0.1151 1.06 0.2918
## RMSE from cross-validation: 1.06337
Variance Inflation Factors
  depth aquaculture dredging runoff sewers structures shipping fisheries
VIF 1.17 1.41 1.35 2.3 1.94 2.65 1.39 1.2

Biomass
## Adjusted R2 is: 0.04
Fitting linear model: B ~ depth + aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.398e-17 0.09425 -5.727e-16 1
depth -0.2062 0.1109 -1.86 0.06585
aquaculture -0.2534 0.1331 -1.904 0.05983
dredging -0.009733 0.1275 -0.07637 0.9393
runoff -0.4785 0.2174 -2.201 0.03008 *
sewers -0.5772 0.1833 -3.149 0.002167 * *
structures 0.5394 0.2509 2.15 0.03399 *
shipping 0.09399 0.1314 0.7154 0.4761
fisheries -0.09747 0.1138 -0.8568 0.3936
## RMSE from cross-validation: 1.018014
Variance Inflation Factors
  depth aquaculture dredging runoff sewers structures shipping fisheries
VIF 1.17 1.41 1.35 2.3 1.94 2.65 1.39 1.2

Diversity
## Adjusted R2 is: 0.29
Fitting linear model: H ~ depth + aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.042e-16 0.08128 4.974e-15 1
depth 0.535 0.09561 5.596 1.956e-07 * * *
aquaculture 0.1666 0.1148 1.451 0.1499
dredging 0.005753 0.1099 0.05234 0.9584
runoff 0.4163 0.1875 2.22 0.02868 *
sewers 0.002447 0.1581 0.01548 0.9877
structures -0.3441 0.2163 -1.59 0.115
shipping 0.1112 0.1133 0.9812 0.3289
fisheries 0.02589 0.0981 0.2639 0.7924
## RMSE from cross-validation: 0.889925
Variance Inflation Factors
  depth aquaculture dredging runoff sewers structures shipping fisheries
VIF 1.17 1.41 1.35 2.3 1.94 2.65 1.39 1.2

Evenness
## Adjusted R2 is: 0.14
Fitting linear model: J ~ depth + aquaculture + dredging + runoff + sewers + structures + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.185e-17 0.08921 9.175e-16 1
depth 0.4573 0.1049 4.358 3.213e-05 * * *
aquaculture 0.06496 0.126 0.5156 0.6073
dredging 0.1359 0.1206 1.126 0.2627
runoff 0.3196 0.2058 1.553 0.1236
sewers 0.06674 0.1735 0.3846 0.7013
structures -0.3271 0.2375 -1.377 0.1715
shipping -0.04701 0.1244 -0.3779 0.7063
fisheries -0.1252 0.1077 -1.163 0.2478
## RMSE from cross-validation: 0.986413
Variance Inflation Factors
  depth aquaculture dredging runoff sewers structures shipping fisheries
VIF 1.17 1.41 1.35 2.3 1.94 2.65 1.39 1.2

4.3. Multivariate regression

The model selected by the DistLM procedure has a \(R^{2}\) of 0.22 (results extracted form PRIMER-e). Colours represent the value of the cumulative exposure index (the bluer, the higher).


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